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Crowd simulation has been used in the entertainment industry to create populated environments that seem more realistic than isolated scenes, specially in urban areas. Video games are no exception, but the simulation of large crowds is often restrained by the remaining compute time available in CPUs, which is often used for most part of the game loop. This paper presents a parallel technique, using consumer grade graphics hardware, for proximity queries that is suitable for real-time crowd simulations. We use a truncated Voronoi diagram and a sampling technique, using ray marching, to find agents' neighbors in an environment texture. The experimental results suggest that our technique has significantly better performance than similar methods and can achieve simulations with thousands of agents in interactive frame rate.

We present a set of algorithms for simulating and visualizing real-time crowds in GPU (Graphics Processing Units) clusters. First we present crowd simulation and rendering techniques that take advantage of single GPU machines. Then, using as an example a wandering crowd behavior simulation algorithm, we explain how this kind of algorithms can be extended for their use in GPU cluster environments. We also present a visualization architecture that renders the simulation results using detailed 3D virtual characters. This architecture is adaptable in order to support the Barcelona Supercomputing Center (BSC) infrastructure. The results show that our algorithms are scalable in different hardware platforms including embedded systems, desktop GPUs, and GPU clusters, in particular, the BSC's Minotauro cluster.

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Abstract:Our objective with this paper is to show how we can couple a group of real people and a simulated crowd of virtual humans. We attach group behaviors to the simulated humans to get a plausible reaction to real people. We use a two stage system: in the first stage, a group of people are segmented from a live video, then a human detector algorithm extracts the positions of the people in the video, which are finally used to feed the second stage, the simulation system. The positions obtained by this process allow the second module to render the real humans as avatars in the scene, while the behavior of additional virtual humans is determined by using a simulation based on a social forces model. Developing the method required three specific contributions: a GPU implementation of the codebook algorithm that includes an auxiliary codebook to improve the background subtraction against illumination changes; the use of semantic local binary patterns as a human descriptor; the parallelization of a social forces model, in which we solve a case of agents merging with each other. The experimental results show how a large virtual crowdreacts to over a dozen humans in a real environment.